Structural modeling of the hERG potassium channel and associated drug interactions
J. Maly, A. M. Emigh, K. R. DeMarco, K. Furutani, J. T. Sack, C. E. Clancy, I. Vorobyov, V. Yarov-Yarovoy
Abstract
The voltage-gated potassium channel, KV11.1, encoded by the human Ether-a-go-go- Related Gene (hERG) is expressed in cardiac myocytes, where it is crucial for the membrane repolarization of the action potential. Gating of hERG channel is characterized by rapid, voltage-dependent, C-type inactivation, which blocks ion conduction and is suggested to involve constriction of the selectivity filter. Mutations S620T and S641A/T within the selectivity filter region of hERG have been shown to alter the voltage- dependence of channel inactivation. Because hERG channel blockade is implicated in drug-induced arrhythmias associated with both the open and inactivated states, we used Rosetta to simulate effects of hERG S620T and S641A/T mutations to elucidate conformational changes associated with hERG channel inactivation and differences in drug binding between the two states. Rosetta modeling of the S641A fast-inactivating mutation revealed a lateral shift of F627 side chain in the selectivity filter into the central channel axis along the ion conduction pathway and formation of four lateral fenestrations in the pore. Rosetta modeling of the non-inactivating mutations S620T and S641T suggested a potential molecular mechanism preventing F627 side chain from shifting into the ion conduction pathway during the proposed inactivation process. Furthermore, we used Rosetta docking to explore the binding mechanism of highly selective and potent hERG blockers - dofetilide, terfenadine, and E4031. Our structural modeling correlate well with existing experimental evidence involving interactions of these drugs with key hERG residues Y652 and F656 inside the pore and reveal potential ligand binding interactions within fenestration region in an inactivated state. Significance StatementComputational models of hERG potassium channel provide structural insights into an inactivated state and associated drug interactions. Our computational approach will be useful to study ion channel modulation by small molecules.
02 /Specification & Classification
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